Abstract P1-07-02: Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of DCIS
Authors: Ying Liu, Siri H. Strand, Lorraine King, Bryan Harmon, Fergus J. Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F. McAuliffe, Jeffrey Marks, Carlo Maley, Robert West, E Shelley Hwang, Graham A. Colditz
Journal: Cancer Research (2023)

Abstract PURPOSE To examine incremental values of estrogen receptor (ER) status, body mass index (BMI), menopausal status, and a previously reported multi-gene classifier over commonly used clinical factors (i.e. age, tumor grade, comedonecrosis, surgical margins, and treatment) in predicting risk of any ipsilateral recurrence (IR) event within five years after DCIS diagnosis. METHODS A derivation cohort consisted of participants in the Translational Breast Cancer Research Consortium (TBCRC) 038, a retrospective multicenter cohort study in women undergoing surgical resection for DCIS between 01/01/1998 and 02/29/2016 (n=216). The validation cohort, the Repository of Archival Human Breast Tissue (RAHBT) at Washington University School of Medicine, provided cases meeting the same eligibility criteria as TBCRC038 (n=97). Participants in both cohorts had RNA-seq data and either developed IR 1-5y after initial DCIS diagnosis or were free from subsequent breast events for at least five years. The previously reported 812-gene classifier had been developed from a subset of the TBCRC038 samples using a negative-binomial regression model to identify differentially expressed genes in the primary tumor associated with subsequent recurrence events. This classifier has been shown to be highly correlated with 5-year invasive, DCIS, and all breast cancer events, and validated in the RAHBT cohort. Cox proportional hazards regression was used to estimate hazard ratios (HRs) of IR in the TBCRC038 cohort (76 with IR). The clinical score was developed using clinical predictors (aforementioned clinical factors and ER) and their regression coefficients from the model with the maximum predictive accuracy (e.g. c-index) and the minimum number of predictors; the summary score integrated the clinical score and multi-gene classifier. Predictive performance of both clinical and summary scores was validated in the RAHBT cohort (20 with IR). RESULTS In the TBCRC cohort derivation set, we used a multivariable model based on clinical factors alone (clinical score) and found that ER status, but not BMI or menopausal status, was independently associated with a higher IR risk (HR=2.06, 95% CI 1.18-3.58). Adding the multi-gene classifier to the clinical factors-based model (summary score) in the TBCRC038 test set increased predictive accuracy (c-index 0.68 to 0.70), with the genomic classifier-adjusted HR of 14.96 (95% CI 8.64-25.91). The summary score had higher predictive performance for IR risk than clinical score alone (c-index 0.82 vs. 0.70). In the RAHBT validation samples, model performance was similarly improved using summary scores clinical factors-based model plus multigene classifier as compared to clinical scores along (c-index 0.74 vs. 0.58). CONCLUSION Combining clinical factors and a multigene classifier provided more accurate risk estimates of IR within five years after excision of DCIS than clinical factors alone. Figure 1. Observed and predicted recurrence-free survival in the first five years after initial DCIS diagnosis in the RAHBT validation cohort, by risk groups defined by clinical scores (left) and clinical score plus multigene classifier (right). Citation Format: Ying Liu, Siri H. Strand, Lorraine King, Bryan Harmon, Fergus J. Couch, Kristalyn Gallagher, Mark Kilgore, Shi Wei, Angela DeMichele, Tari King, Priscilla F. McAuliffe, Jeffrey Marks, Carlo Maley, Robert West, E Shelley Hwang, Graham A. Colditz. Using clinical characteristics and molecular markers to predict the risk of subsequent ipsilateral breast events after excision of DCIS [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-07-02.